"""simple docstring"""


import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import (
        MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
        TimesformerForVideoClassification,
        TimesformerModel,
    )
    from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from transformers import VideoMAEImageProcessor


class 							a					:


    def __init__(       self						:	Optional[Any] ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	Dict=13 ,					__lowerCAmelCase						:	int=10 ,					__lowerCAmelCase						:	Any=3 ,					__lowerCAmelCase						:	Optional[int]=2 ,					__lowerCAmelCase						:	int=2 ,					__lowerCAmelCase						:	List[Any]=True ,					__lowerCAmelCase						:	Any=True ,					__lowerCAmelCase						:	List[Any]=32 ,					__lowerCAmelCase						:	Dict=5 ,					__lowerCAmelCase						:	Optional[int]=4 ,					__lowerCAmelCase						:	Optional[Any]=37 ,					__lowerCAmelCase						:	Any="gelu" ,					__lowerCAmelCase						:	Any=0.1 ,					__lowerCAmelCase						:	Any=0.1 ,					__lowerCAmelCase						:	Optional[int]=10 ,					__lowerCAmelCase						:	Optional[Any]=0.02 ,					__lowerCAmelCase						:	Dict="divided_space_time" ,					__lowerCAmelCase						:	int=None ,					):
      _UpperCAmelCase    			=   parent
      _UpperCAmelCase    			=   batch_size
      _UpperCAmelCase    			=   image_size
      _UpperCAmelCase    			=   num_channels
      _UpperCAmelCase    			=   patch_size
      _UpperCAmelCase    			=   num_frames
      _UpperCAmelCase    			=   is_training
      _UpperCAmelCase    			=   use_labels
      _UpperCAmelCase    			=   hidden_size
      _UpperCAmelCase    			=   num_hidden_layers
      _UpperCAmelCase    			=   num_attention_heads
      _UpperCAmelCase    			=   intermediate_size
      _UpperCAmelCase    			=   hidden_act
      _UpperCAmelCase    			=   hidden_dropout_prob
      _UpperCAmelCase    			=   attention_probs_dropout_prob
      _UpperCAmelCase    			=   attention_type
      _UpperCAmelCase    			=   initializer_range
      _UpperCAmelCase    			=   scope
      _UpperCAmelCase    			=   num_labels

      # in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
      _UpperCAmelCase    			=   (image_size // patch_size) ** 2
      _UpperCAmelCase    			=   (num_frames) * self.num_patches_per_frame + 1


    def   lowerCAmelCase_	(       self						:	Tuple       ):
      _UpperCAmelCase    			=   floats_tensor(
          [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]       )

      _UpperCAmelCase    			=   None
      if self.use_labels:
        _UpperCAmelCase    			=   ids_tensor([self.batch_size] ,					self.num_labels       )

      _UpperCAmelCase    			=   self.get_config()

      return config, pixel_values, labels


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   TimesformerConfig(
          image_size=self.image_size ,					patch_size=self.patch_size ,					num_channels=self.num_channels ,					num_frames=self.num_frames ,					hidden_size=self.hidden_size ,					num_hidden_layers=self.num_hidden_layers ,					num_attention_heads=self.num_attention_heads ,					intermediate_size=self.intermediate_size ,					hidden_act=self.hidden_act ,					hidden_dropout_prob=self.hidden_dropout_prob ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob ,					initializer_range=self.initializer_range ,					attention_type=self.attention_type ,					)
      _UpperCAmelCase    			=   self.num_labels
      return config


    def   lowerCAmelCase_	(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	Dict       ):
      _UpperCAmelCase    			=   TimesformerModel(config=__snake_case       )
      model.to(__snake_case       )
      model.eval()
      _UpperCAmelCase    			=   model(__snake_case       )
      self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)       )


    def   lowerCAmelCase_	(       self						:	List[Any] ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	List[Any]       ):
      _UpperCAmelCase    			=   TimesformerForVideoClassification(__snake_case       )
      model.to(__snake_case       )
      model.eval()

      _UpperCAmelCase    			=   model(__snake_case       )

      # verify the logits shape
      _UpperCAmelCase    			=   torch.Size((self.batch_size, self.num_labels)       )
      self.parent.assertEqual(result.logits.shape ,					__snake_case       )


    def   lowerCAmelCase_	(       self						:	Any       ):
      _UpperCAmelCase    			=   self.prepare_config_and_inputs()
      _UpperCAmelCase    			=   config_and_inputs
      _UpperCAmelCase    			=   {'pixel_values': pixel_values}
      return config, inputs_dict



@require_torch
class 							a					(		lowerCamelCase__						,				lowerCamelCase__						,				unittest.TestCase ):
    _snake_case  :	Dict  				=				(TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
    _snake_case  :	Tuple  				=				(
        {'feature-extraction': TimesformerModel, 'video-classification': TimesformerForVideoClassification}
        if is_torch_available()
        else {}
    )

    _snake_case  :	Dict  				=				False
    _snake_case  :	Optional[Any]  				=				False
    _snake_case  :	Optional[int]  				=				False
    _snake_case  :	Dict  				=				False


    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   TimesformerModelTester(self       )
      _UpperCAmelCase    			=   ConfigTester(
          self ,					config_class=__snake_case ,					has_text_modality=__snake_case ,					hidden_size=37       )


    def   lowerCAmelCase_	(       self						:	str ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	Optional[int]=False       ):
      _UpperCAmelCase    			=   copy.deepcopy(__snake_case       )

      if return_labels:
        if model_class in get_values(__snake_case       ):
          _UpperCAmelCase    			=   torch.zeros(
              self.model_tester.batch_size ,					dtype=torch.long ,					device=__snake_case       )

      return inputs_dict


    def   lowerCAmelCase_	(       self						:	int       ):
      self.config_tester.run_common_tests()


    @unittest.skip(reason="""TimeSformer does not use inputs_embeds"""       )
    def   lowerCAmelCase_	(       self						:	Tuple       ):
      pass


    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        _UpperCAmelCase    			=   model_class(__snake_case       )
        self.assertIsInstance(model.get_input_embeddings() ,					(nn.Module)       )
        _UpperCAmelCase    			=   model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(__snake_case ,					nn.Linear       )       )


    def   lowerCAmelCase_	(       self						:	Dict       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        _UpperCAmelCase    			=   model_class(__snake_case       )
        _UpperCAmelCase    			=   inspect.signature(model.forward       )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        _UpperCAmelCase    			=   [*signature.parameters.keys()]

        _UpperCAmelCase    			=   ['pixel_values']
        self.assertListEqual(arg_names[:1] ,					__snake_case       )


    def   lowerCAmelCase_	(       self						:	Dict       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__snake_case       )


    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_video_classification(*__snake_case       )


    @slow
    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        _UpperCAmelCase    			=   TimesformerModel.from_pretrained(__snake_case       )
        self.assertIsNotNone(__snake_case       )


    def   lowerCAmelCase_	(       self						:	Tuple       ):
      if not self.has_attentions:
        pass

      else:
        _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase    			=   True

        for model_class in self.all_model_classes:
          _UpperCAmelCase    			=   self.model_tester.seq_length
          _UpperCAmelCase    			=   self.model_tester.num_frames

          _UpperCAmelCase    			=   True
          _UpperCAmelCase    			=   False
          _UpperCAmelCase    			=   True
          _UpperCAmelCase    			=   model_class(__snake_case       )
          model.to(__snake_case       )
          model.eval()
          with torch.no_grad():
            _UpperCAmelCase    			=   model(**self._prepare_for_class(__snake_case ,					__snake_case       )       )
          _UpperCAmelCase    			=   outputs.attentions
          self.assertEqual(len(__snake_case       ) ,					self.model_tester.num_hidden_layers       )

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          _UpperCAmelCase    			=   True
          _UpperCAmelCase    			=   model_class(__snake_case       )
          model.to(__snake_case       )
          model.eval()
          with torch.no_grad():
            _UpperCAmelCase    			=   model(**self._prepare_for_class(__snake_case ,					__snake_case       )       )
          _UpperCAmelCase    			=   outputs.attentions
          self.assertEqual(len(__snake_case       ) ,					self.model_tester.num_hidden_layers       )

          # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
          self.assertListEqual(
              list(attentions[0].shape[-3:]       ) ,					[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] ,					)
          _UpperCAmelCase    			=   len(__snake_case       )

          # Check attention is always last and order is fine
          _UpperCAmelCase    			=   True
          _UpperCAmelCase    			=   True
          _UpperCAmelCase    			=   model_class(__snake_case       )
          model.to(__snake_case       )
          model.eval()
          with torch.no_grad():
            _UpperCAmelCase    			=   model(**self._prepare_for_class(__snake_case ,					__snake_case       )       )

          self.assertEqual(out_len + 1 ,					len(__snake_case       )       )

          _UpperCAmelCase    			=   outputs.attentions

          self.assertEqual(len(__snake_case       ) ,					self.model_tester.num_hidden_layers       )

          # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
          self.assertListEqual(
              list(self_attentions[0].shape[-3:]       ) ,					[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] ,					)


    def   lowerCAmelCase_	(       self						:	int       ):
      def check_hidden_states_output(__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	Union[str, Any]       ):
        _UpperCAmelCase    			=   model_class(__snake_case       )
        model.to(__snake_case       )
        model.eval()

        with torch.no_grad():
          _UpperCAmelCase    			=   model(**self._prepare_for_class(__snake_case ,					__snake_case       )       )

        _UpperCAmelCase    			=   outputs.hidden_states
        _UpperCAmelCase    			=   self.model_tester.num_hidden_layers + 1
        self.assertEqual(len(__snake_case       ) ,					__snake_case       )

        _UpperCAmelCase    			=   self.model_tester.seq_length

        self.assertListEqual(
            list(hidden_states[0].shape[-2:]       ) ,					[seq_length, self.model_tester.hidden_size] ,					)

      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        _UpperCAmelCase    			=   True
        check_hidden_states_output(__snake_case ,					__snake_case ,					__snake_case       )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        _UpperCAmelCase    			=   True

        check_hidden_states_output(__snake_case ,					__snake_case ,					__snake_case       )






def      __UpperCAmelCase       (      ):







  """simple docstring"""
  _UpperCAmelCase    			=   hf_hub_download(
      repo_id="""hf-internal-testing/spaghetti-video""" ,filename="""eating_spaghetti.npy""" ,repo_type="""dataset"""		)
  _UpperCAmelCase    			=   np.load(_A		)
  return list(_A		)


@require_torch
@require_vision
class 							a					(		unittest.TestCase ):


    @cached_property
    def   lowerCAmelCase_	(       self						:	Any       ):
      # logits were tested with a different mean and std, so we use the same here
      return (
          VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] ,					image_std=[0.5, 0.5, 0.5]       )
          if is_vision_available()
          else None
      )


    @slow
    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   TimesformerForVideoClassification.from_pretrained("""facebook/timesformer-base-finetuned-k400"""       ).to(
          __snake_case       )

      _UpperCAmelCase    			=   self.default_image_processor
      _UpperCAmelCase    			=   prepare_video()
      _UpperCAmelCase    			=   image_processor(video[:8] ,					return_tensors="""pt"""       ).to(__snake_case       )

      # forward pass
      with torch.no_grad():
        _UpperCAmelCase    			=   model(**__snake_case       )

      # verify the logits
      _UpperCAmelCase    			=   torch.Size((1, 400)       )
      self.assertEqual(outputs.logits.shape ,					__snake_case       )

      _UpperCAmelCase    			=   torch.tensor([-0.3_016, -0.7_713, -0.4_205]       ).to(__snake_case       )

      self.assertTrue(torch.allclose(outputs.logits[0, :3] ,					__snake_case ,					atol=1e-4       )       )





'''simple docstring'''






from __future__ import annotations
def  lowerCamelCase__			(		_A					,     _A					,     _A							):
  a		:   List[str]	  =	list(range(len(_A							)							)							)
  a		:   Union[str, Any]	  =	[v / w for v, w in zip(_A					,     _A							)]
  index.sort(key=lambda _A							: ratio[i]					,     reverse=_A							)

  a		:   float	  =	0
  a		:   list[float]	  =	[0] * len(_A							)
  for i in index:
    if weight[i] <= capacity:
      a		:   int	  =	1
      max_value += value[i]
      capacity -= weight[i]
    else:
      a		:   Optional[int]	  =	capacity / weight[i]
      max_value += value[i] * capacity / weight[i]
      break

  return max_value, fractions


if __name__ == "__main__":
     import doctest

     doctest.testmod()